Measuring visual closeness of 3-D models is an important issue for different problems and there is still no standardized metric or algorithm to do it.
The normal of a surface plays a vital role in the shading of a 3-D object. Motivated by this, we developed two applications to measure visualcloseness, introducing normal difference as a parameter in a weighted metric in Metro’s sampling approach to obtain the maximum and mean distance between 3-D models using 3-D and 6-D correspondence search structures.
A visual closeness metric should provide accurate information on what the human observers would perceive as visually close objects. We performed
a validation study with a group of people to evaluate the correlation of our
metrics with subjective perception. The results were positive since the metrics
predicted the subjective rankings more accurately than the Hausdorff
distance.
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/248714 |
| Date | 09 1900 |
| Creators | Gollaz Morales, Jose Alejandro |
| Contributors | Vigneron, Antoine, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division, Cao, Yuanhao, Rockwood, Alyn |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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